Modern-Era Retrospective Analysis For Research And Applications Research Articles

Unusual Changes in the Antarctic Middle Atmosphere During the 2019 Warming in the Southern Hemisphere

AbstractA rare sudden stratosphere warming (SSW) occurred in the Southern Hemisphere polar region in 2019. The polar stratosphere temperature and planetary wave (PW) enhancements are found to be unusual from the history for 40 years; hence, it is an “Extremely‐Rare” SSW. The distinct features of the mesosphere winds were observed during the SSW, in association with the traveling PWs in the stratosphere. The mesosphere zonal winds reversed for about 20 days before the peak SSW. Meteor radar (MR) and Modern‐Era Retrospective Analysis for Research and Applications (MERRA)‐2 observations indicate that the zonal wind reversal was descended with time, and the reversal was larger over ~72°S than the MR site (62°S). The MR detected the PWs of 14–22 days before and 8–12 days following the SSW in the mesosphere. We further noticed the enhancement of wavenumber 1 signature in the mesosphere during the peak SSW over the polar region. Thus, the polar middle‐atmosphere is greatly affected by the SSW.

How much snow falls in the world's mountains? A first look at mountain snowfall estimates in A-train observations and reanalyses

Abstract. CloudSat estimates that 1773 km3 of snow falls, on average, each year over the world's mountains. This amounts to 5 % of the global snowfall accumulations. This study synthetizes mountain snowfall estimates over the four continents containing mountains (Eurasia, North America, South America and Africa), comparing snowfall estimates from a new satellite cloud-radar-based dataset to those from four widely used reanalyses: Modern-Era Retrospective analysis for Research and Applications (MERRA), MERRA-2, Japanese 55-year Reanalysis (JRA-55), and European Center for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim). Globally, the fraction of snow that falls in the world's mountains is very similar between all these independent datasets (4 %–5 %), providing confidence in this estimate. The fraction of snow that falls in the mountains compared to the continent as a whole is also very similar between the different datasets. However, the total of snow that falls globally and over each continent – the critical factor governing freshwater availability in these regions – varies widely between datasets. The consensus in fractions and the dissimilarities in magnitude could indicate that large-scale forcings may be similar in the five datasets, while local orographic enhancements at smaller scales may not be captured. This may have significant implications for our ability to diagnose regional trends in snowfall and its impacts on snowpack in rapidly evolving alpine environments.

NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology

Satellite remote sensing and model data play an important role in research and applications of tropical meteorology and climatology over vast, data-sparse oceans and remote continents. Since the first weather satellite was launched by NASA in 1960, a large collection of NASA’s Earth science data is freely available to the research and application communities around the world, significantly improving our overall understanding of the Earth system and environment. Established in the mid-1980s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), located in Maryland, USA, is a data archive center for multidisciplinary, satellite and model assimilation data products. As one of the 12 NASA data centers in Earth sciences, GES DISC hosts several important NASA satellite missions for tropical meteorology and climatology such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) Mission and the Modern-Era Retrospective analysis for Research and Applications (MERRA). Over the years, GES DISC has developed data services to facilitate data discovery, access, distribution, analysis and visualization, including Giovanni, an online analysis and visualization tool without the need to download data and software. Despite many efforts for improving data access, a significant number of challenges remain, such as finding datasets and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. In this article, we list and describe major NASA satellite remote sensing and model datasets and services for tropical meteorology and climatology along with examples of using the data and services, so this may help users better utilize the information in their research and applications.

Evaluation of two satellite-based products against ground-based observation for drought analysis in the southern part of Iran

Water stress or more specifically drought assessment plays a key role in water management, especially in extreme climate conditions. Basically, globally gridded satellite-based precipitation products are potential sources of data as alternatives for ground-based measurements. However, for a reliable application, they should be evaluated in different regions. In this paper, two satellite-based rainfall products, namely Modern-Era Retrospective Analysis for Research and Applications (MERRA)-Land and Global Land Data Assimilation System-2 (GLDAS-2), have been evaluated against ground-based observations in terms of precipitation and their application for drought analysis. At first, the coarse-resolution MERRA-Land is downscaled to the finer resolution of interest for better comparison. After comparison of these datasets against ground-based observations in terms of precipitation, it is concluded that MERRA-Land can better estimate precipitation. Then, the nonparametric SPIs at various timescales are derived to analyze how well MERRA-Land performs in drought monitoring. Different categorical and statistical error indices are used to assess the efficiency of MERRA-Land in capturing drought events. The results revealed that the downscaled MERRA-Land data can properly detect short-term and mid-term drought events known as agricultural and meteorological droughts throughout the study area, respectively. In addition, drought maps show that the majority of lands experience mid-term scale drought which are in agreement with ground-based observations. The methodology adopted in this study can be applied in areas lacking in rain-gauge stations which significantly extend current capabilities for drought monitoring and early warning systems.

Performance of hydrostatic and non‐hydrostatic dynamical cores in RegCM4.6 for Indian summer monsoon simulation

AbstractThe efficacy of regional climate model RegCM4.6 using hydrostatic core resolutions at 36km (HY36) and 12km (HY12) and a non‐hydrostatic core resolution at 12km (NH12) is investigated by simulating the normal, excess and deficit monsoon seasons. The ERA‐Interim reanalysis data are used to drive the model and the India Meteorological Department (IMD) and modern‐era retrospective analysis for research and applications (MERRA) rainfall data are used for precipitation verification. The heavy rainfall regions are well simulated in the high‐ compared with the coarse‐resolution simulations, with the maximum in the NH12. The non‐hydrostatic dynamics amalgamate the vertical acceleration with the orographic uplifting that causes more precipitation over hilly regions than that of the hydrostatic core. On the other hand, the lesser precipitation over northwest India is better portrayed in the HY12 than in the other two. Over central India, the HY36 performs better followed by the NH12; and the contrasting precipitation features are also well depicted in the HY36 and NH12. This is probably because of the better representation of large‐scale monsoon features, such as a monsoon trough in the HY36 and local‐scale convective activities in the NH12. Daily rainfall analysis also shows that the high‐resolution model is capable of capturing the active and break phases during the El Niño and La Niña seasons. The non‐hydrostatic model possesses good correlation co‐efficients >0.5 over the hydrostatic model with co‐efficients of 0.35. The analysis of upper air circulations and the derived parameters, including statistical tests, confirm that the RegCM4.6 with non‐hydrostatics is useful for orographic regions, hydrostatic at a coarse resolution and non‐hydrostatic at a finer resolution and could be suitable for plain regions.

Validation of global irradiance derived from INSAT-3D over India

Geostationary satellites offer the possibility of producing high-frequency time series irradiance datasets, covering a large geographical area. Here, we derive half-hourly global irradiance (GHI) at 4 km spatial resolution from visible channel radiance of INSAT-3D satellite using a semi-empirical model and validate it against 19 ground stations over 2015–16, across India. Our model uses the Advanced Solis clear-sky model with monthly climatological values of modern-era retrospective analysis for research and applications-2 (MERRA-2) aerosol optical depth (AOD) and precipitable water (PW) as inputs. The relative root mean squared error (rRMSE) and relative mean bias error (rMBE) are 24.02% and −0.05%, respectively. Over the validation period, INSAT-3D derived GHI outperformed SARAH-E model and MERRA-2 reanalysis GHI.

Comparison of satellite remote sensing derived precipitation estimates and observed data in Kenya

This study evaluated the accuracy of four satellite remote sensing (SRS) based products in predicting rainfall (amounts and spatial distribution) over Kenya between 1998 and 2013. The four SRS products used include; two satellite products (Climate Hazards Group InfraRed Precipitation with Station (CHIRPS 2.0) and Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA) 3B42 version 7 (TRMM)), one gauge-interpolated product (Global Precipitation Climatology Centre (GPCC)) and one re-analysis product (Modern-Era Retrospective Analysis for Research and Application (MERRA)). The monthly precipitation data were evaluated for completeness, converted to individual raster files, projected to the World Geodetic System (WGS) 1984 - Universal Transverse Mercator (UTM) zone 37 N ensuring a similar processing extent, rescaled to a common resolution and reclassified following defined uniform intervals for ease of comparison. Thereafter, they were subjected to five different metrics based on eight agro-ecological zones (AEZs) of Kenya, in reference to observed rainfall data obtained from Kenya meteorological department (KMD). Results show that all SRS products both overestimated or underestimated rainfall amounts on a pixel to pixel comparison. Based on point to point proportion of variance evaluation (r2), TRMM best-estimated rainfall in the tropical cool humid (r2 = 0.64), tropical warm humid (r2 = 0.58) and tropical cool subhumid (r2 = 0.39) zones and can be used for agricultural advisory services. The GPCC product best-estimated rainfall in the tropical warm semiarid (r2 = 0.46) and warm tropical sub-humid (r2 = 0.21), while CHIRPS 2.0 best-estimated rainfall in the tropical warm arid (r2 = 0.33) and therefore the two products could be best used to predict rainfall in the ASALs and drought-related studies, with potential for irrigation. The MERRA product best-estimated rainfall in tropical cool arid (r2 = 0.97) and tropical cool semiarid (r2 = 0.53) and could, therefore, be best used for high elevation and drought-related studies. These results demonstrate the promising potential of the satellite remote sensed data in complementing the existing meteorological observed data which are often marred by inconsistency and scarcity, and hence unreliable in the existing agricultural advisory and other climate-based applications in Kenya, and sub-Saharan Africa at large. However, given the observed AEZ dependant variations in the satellite estimates, it is advisable to choose the most suitable SRS product for specific activities per AEZ and calibrate before utilisation.

Evaluation of Near-Surface Wind Speed Changes during 1979 to 2011 over China Based on Five Reanalysis Datasets

Wind speed data derived from reanalysis datasets has been used in the plan and design of wind farms in China, but the quality of these kinds of data over China remains unknown. In this study, the performances of five sets of reanalysis data, including National Centers for Environmental Predictions (NCEP)-U.S. Department of Energy (DOE) Reanalysis 2 (NCEP-2), Modern-ERA Retrospective Analysis for Research and Applications (MERRA), Japanese 55-year Reanalysis Project (JRA-55), Interim ECMWF Re-Analysis product (ERA-Interim), and 20th Century Reanalysis (20CR) in reproducing the climatology, interannual variation, and long-term trend of near-surface (10 m above ground) wind speed, for the period of 1979–2011 over continental China are comprehensively evaluated. Compared to the gridded data compiled from meteorological stations, all five reanalysis datasets reasonably reproduce the spatial distribution of the climatology of near-surface wind speed, but underestimate the intensity of the near-surface wind speed in most regions except for Tibetan Plateau where the wind speed is overestimated. All five reanalysis datasets show large weaknesses in reproducing the annual cycle of near-surface wind speed averaged over the continental China. The near-surface wind speed derived from the observations exhibit significant decreasing trends over most parts of continental China during 1979 to 2011. Although the spatial patterns of the linear trends reproduced by reanalysis datasets are close to the observation, the magnitudes are weaker in annual, spring, summer and autumn season. The qualities of all reanalysis datasets are limited in winter. For the interannual variability, except for winter, all five reanalysis datasets reasonably reproduce the interannual standard deviation but with larger amplitude. Quantitative comparison indicates that among the five reanalysis datasets, the MERRA (JRA-55) shows the relatively highest (lowest) skill in terms of the climatology and linear trend. These results call for emergent needs for developing high quality reanalysis data that can be used in wind resource assessment and planning.

Melting Layer Detection and Characterization based on Range Height Indicator–Quasi Vertical Profiles

The melting layer (ML) is an important region used to describe the transition of hydrometeors from the solid to the liquid phase. It is a typical feature used to characterize the vertical structure of the stratiform precipitation. The present study implements a new automatic melting-layer detection algorithm based on the range-height-indicator–quasi-vertical profile (R-QVP) in the X-band dual-polarization radars. The algorithm uses the gradients of the polarimetric radar variables reflectivity factor at horizontal polarization (Zh), differential reflectivity (Zdr), and copolar correlation coefficient (ρhv), and their combinations to describe the ML characteristics. The melting layer heights derived from the radar were compared and validated with the heights of the 0 °C wet-bulb temperature derived from the Modern-Era retrospective analysis for research and applications (MERRA) reanalysis datasets and obtained high correlation coefficient 0.96. The R-QVP combined with this algorithm led to spatial and temporal variabilities of the melting layer thickness. The thickness of the melting layer was independent of the seasonal, spatial, and temporal variabilities of the precipitations. Intriguing polarimetric signatures have been observed inside, above, and below the ML, based on the phase of the precipitation particles. The statistics of the polarimetric variables were evaluated for ML, rain, and snow. Further, the linkage between enhanced specific differential phase shift (Kdp) and Zdr in the dendritic growth layer (DGL) and surface precipitation was also described.

Spatiotemporal Investigations of Aerosol Optical Properties Over Bangladesh for the Period 2002–2016

This study investigates the aerosol optical properties over Bangladesh using Terra MODIS-based collection 06 (DT and DB) aerosol optical depth (AOD), ozone monitoring instrument (OMI)-based aerosol absorption optical depth (AAOD), emission database for global atmospheric research (EDGAR) measured black carbon (BC) and organic carbon (OC), and modern-era retrospective analysis for research and applications (MERRA) retrieved dust. In addition, ground-based aerosol robotic network (AERONET) retrieved optical properties such as aerosol volume size distribution, single scattering albedo (SSA), and asymmetry parameter (ASY) are studied in understanding the behavior of aerosol properties. Both the satellite-based MODIS DB and DT algorithms detect the high AOD (> 0.5) all over Bangladesh except for a small portion in the eastern side. High AOD is also observed in all seasons except for SON. AOD significantly (at 95% level) increased over the period 2002–2016. The correlation coefficient between MODIS and AERONET AOD at Dhaka University site is 0.78 (0.76) for DT (DB). The Expected Error envelope is found 75.70% (54.38%) with small (large) RMSE for DT (DB) product. OMI-based AAOD indicates the presence of absorbing aerosols over the study area which is confirmed with AEROENT-based SSA and ASY. Three different types of absorbing aerosols such as BC, OC, and dust are identified from the EDGAR and MERRA data. In Bangladesh, the BC, OC, and dust are significantly (at 95% level) increasing. Further work is suggested to simulate and assess aerosols against the observations, which will help projecting aerosols in the future climate.

Evaluation of four different climate sources on pavement mechanistic-empirical design and impact of surface shortwave radiation

ABSTRACT This paper compares the predicted distresses (via in the AASHTOWare Pavement ME Design software (PMED)) of asphalt concrete (AC) and jointed plain concrete pavement (JPCP) using four different climate data sources. These sources also include the Modern Era Retrospective Analysis for Research and Application (MERRA) Versions 1.0 and 2.0 (MERRA-1 and MERRA-2). Pavement performance predictions generated using these data showed disagreement among some of the climate data sources, especially for MERRA-2. Comprehensive diurnal and other time-series analyses of the raw climate data found significant disagreements in the percent sunshine estimates. Percent sunshine is used in the PMED environmental effects model to semi-empirically estimate the downwelling shortwave radiation reaching the pavement surface. Both MERRA data (MERRA -1 and MERRA-2) independently provide direct predictions of downwelling surface shortwave radiation (SSR). The direct model predictions of SSR were used to back calculate ‘synthetic’ per cent sunshine for input into the PMED. The use of the synthetic per cent sunshine derived from the predicted SSR eliminated nearly all discrepancies in the predicted pavement performance using MERRA-1 versus MERRA-2 data. Based on these results, the authors recommend that SSR rather than per cent sunshine be used as a direct input to the PMED.

What global reanalysis best represents near‐surface winds?

Since global reanalysis datasets first appeared in the 1990s, they have become an essential tool to understand the climate of the past. The wind power industry uses those products extensively for wind resource assessment, while several climate services for energy rely on them as well. Nowadays various datasets coexist, which complicates the selection of the most suitable source for each purpose. In an effort to identify the products that best represent the wind speed features at turbine hub heights, five state‐of‐the‐art global reanalyses have been analysed: ERA5, ERA‐Interim, the Japanese 55‐year Reanalysis (JRA55), the Modern Era Retrospective Analysis for Research and Applications‐2 (MERRA2), and the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis 1 (R1). A multi‐reanalysis ensemble approach is used to explore the main differences amongst these datasets in terms of surface wind characteristics. Then, the quality of the surface and near‐surface winds is evaluated with a set of 77 instrumented tall towers. Results reveal that important discrepancies exist in terms of boreal winter seasonal means, interannual variability (IAV), and decadal linear trends. The differences in the computation of these parameters, which are mainly concentrated inland, reach up to the order of magnitude of the parameters themselves. Comparison with in situ observations shows that the ERA5 surface winds offer the best agreement, correlating and reproducing the observed variability better than a multi‐reanalysis mean in 35.1% of the tall tower sites on a daily time‐scale. However, none of the reanalyses stands out from the others when comparing seasonal mean winds. Regarding the IAV, near‐surface winds from ERA5 offer the values closest to the observed IAV.

The nature and origins of the day-to-day variability in Earth’s surface magnetic field

Numerical experiments are performed with the National Center for Atmospheric Research (NCAR) Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) to reveal the characteristics and origins of daytime magnetic field variations on the ground (ΔB) at planetary-wave (PW) periods (2–20 days). Simulations are performed to separate the responses to forcing in the lower atmosphere from solar-magnetospheric forcing. Lower-atmosphere forcing is specified at the 97-km lower boundary of the TIE-GCM by NCAR's Thermosphere-Ionosphere-Mesosphere Electrodynamics General Circulation Model (TIME-GCM), which itself is forced at 30 km by MERRA (Modern Era Retrospective-analysis for Research and Applications) outputs. Solar and magnetospheric inputs to the TIE-GCM are specified according to parameterizations based on F10.7 and Kp. The study focuses on latitudes 0°–65°N during October 1–31, 2009, when F10.7 (range 68–80), Kp (range 0–4), and Ap (range 0–13) are typical of quiet-time “weather”. Neutral dynamics in the dynamo region (ca. 100–150 km) during this period is dominated by winds due to PW modulated tides, where the PW include the quasi-6, 10 and 16-day westward-propagating normal modes with zonal wavenumber s = 1, and eastward-propagating ultra-fast Kelvin waves (UFKW) with s = −1 and periods between 2 and 5 days.Results and conclusions are as follows. PW-period perturbations in daytime ΔB at the ground are dominated by variability originating in the lower atmosphere. The only exception is the 45°–65° latitude regime around noon, where the ΔB variability due to lower atmospheric forcing exceeds that due to solar-magnetospheric forcing by only about 50%. Broadband zonally-symmetric oscillations also occur in ΔB due to dissipation of the tidal spectrum at PW periods in the E-region. These results raise the possibility that some level of contamination from the lower atmosphere may exist in magnetic indices such as ap, Kp, and Ap that are used as measures solar-magnetosphere-ionosphere coupling strength, under levels of geomagnetic activity similar to that characterizing October 2009. It is also found that variations in conductivities play a minor role compared with neutral winds in producing PW-period variations in ΔB, and that there is not a robust one-to-one correspondence between spectral peaks in ΔB and those in the neutral winds. Several factors contribute to this latter result, which are explained in the text.

Revisiting Moisture Transport During the Indian Summer Monsoon Using the Moisture River Concept

The moisture river (MR) concept is used here to revisit the characteristics of moisture transport during the Indian Summer Monsoon (ISM) season from June to September. The study is carried out using Modern-Era Retrospective analysis for Research and Applications (MERRA) data products for the 30-year period from 1982 to 2011. MRs are detected from vertically integrated vapor transport (IVT) computed using validated MERRA mixing ratios and horizontal wind speeds at vertical levels from the surface to 300 hPa. The characteristics of MR and moisture transport associated with major rainfall episodes of the 2011 ISM are studied in detail. The large moisture content, together with high wind speeds associated with the monsoon low-level jet, contributes to the observed structure of the MR. The outflux of moisture from the Arabian Sea is on average 40% larger than the cross-equatorial flux from the southern Indian Ocean, indicating increased contribution from the Arabian Sea. The upward movement of moisture to the layers above the jet is a decisive factor in the strengthening of the river and major rainfall episodes. Back-trajectory analyses reveal that the path of moisture transport from the southern Indian Ocean is confined to the lower levels, whereas higher-level trajectories originate from the Red Sea, Arabian Sea and southern Indian Ocean. The MR statistics show significant intra-seasonal and inter-annual variability in occurrence and strength, with a near-linear relationship between the IVT strength and the corresponding daily ISM rainfall, especially over the west coast. Wavelet cross-spectral analyses carried out using the daily Arabian Sea IVT maximum and rainfall over the west coast and central India during a deficit and surplus monsoon year depict significant differences in the dominant periodicities, associated with intra-seasonal and inter-annual monsoon variations.

Comparative Analysis between Modern-Era Retrospective Analysis for Research and Applications and Updated Mechanistic-Empirical Pavement Design Guide Climate Database in the State of Tennessee

The Mechanistic-Empirical Pavement Design Guide addresses climate effects on pavement design in a comprehensive way, which allows for investigating the effect of climate on pavement performance. However, it requires detailed climate inputs, which might not be readily available for most of the state departments of transportation. The AASHTOWare Pavement Mechanistic-Empirical Design (PMED) version 2.3 (v2.3) climate database encompasses 12 weather stations in the state of Tennessee, which does not satisfactorily represent all climatic regions in the state. The terrain in Tennessee varies from flat in the west to mountainous in the east. To evaluate the effectiveness of the updated AASHTOWare PMED v2.3 climate data input, this study analyses the performance of selected pavements in the state of Tennessee using the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and the AASHTOWare PMED v2.3 databases as sources of PMED climate data inputs. A comparative analysis of the two climate data sources is conducted using eight long-term pavement performance (LTPP) sites in the state of Tennessee. The study revealed that MERRA as a climate data source for the state of Tennessee offers better geographic coverage, and therefore provides more precise distress predictions than the AASHTOWare PMED v2.3 climate database.

An assessment of recent global atmospheric reanalyses for Antarctic near surface air temperature

The quality-controlled monthly air temperature records from 32 manned stations and 10 automatic weather stations are used to assess the performance of Climate Forecast System Reanalysis (CFSR), the ECMWF “Interim” reanalysis (ERA-Interim), the Japan Meteorological Agency 55-year Reanalysis (JRA-55), the Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2), and four twentieth century reanalysis data sets (20CR, ERA-20C, CERA-20C and ERA-20CM). The performance of the datasets for Antarctic air temperature differs on different time scales and with glaciological regime. In general, these datasets have higher mean absolute error (MAE) in the winter months and lower MAE in the summer months. Overall, MERRA-2 presents highest performance for monthly air temperature values, with MAE <2 °C at any month. ERA-Interim best represents the inter-annual variability in monthly air temperature for the 1979–2010 period. In the 20th century datasets, changes in temperature prior to 1979 are more uncertain than variability after 1979. Furthermore, substantial inhomogeneities occur in the 20th century datasets, and as a result, their temperature trends are found to be largely spurious.

Spatiotemporal variability of the wind power resource in Argentina and Uruguay

AbstractThe spatiotemporal variability of the wind power resource over Argentina and Uruguay is assessed based on the Modern‐Era Retrospective Analysis for Research and Applications 2 (MERRA2) dataset. Hourly wind speeds were interpolated to 100‐m height, and then, wind power outputs were computed using power curves of three International Electrotechnical Commission (IEC) wind classes. The time series of wind power outputs were filtered using a fast Fourier transform (FFT) to separate regular (annual and daily) from irregular (interannual and synoptic scale) cycles. An empirical orthogonal function analysis was applied to the resulting datasets to obtain the main modes of variability. The results show that the combination of wind power outputs from southern and northern Patagonia broadly follows the average annual electric load. Patagonia exhibits the highest variability on the interannual, annual, and synoptic timescales. On the interannual and synoptic timescales, the variability modes are associated with known and distinct atmospheric circulation modes. The interannual modes of variability are associated with opposite surface level pressure (SLP) anomalies between middle and high latitudes.

Examining the capability of reanalyses in capturing the temporal clustering of heavy precipitation across Europe

The aim of this study is to examine the capability of reanalyses in capturing the temporal clustering of heavy precipitation over Europe. We focus on four widely used products: the National Aeronautics and Space Administration’s Modern Era Retrospective-Analysis for Research and Applications (MERRA) and version 2 (MERRA2); European Centre for Medium-Range Weather Forecasts’ ERA-Interim; Japanese Meteorological Agency’s Japanese 55-year Reanalysis (JRA-55). We define as heavy precipitation days those exceeding the 95th percentile of the precipitation distribution at every pixel, and investigate the temporal clustering in these events with respect to the Arctic Oscillation (AO) or the North Atlantic Oscillation (NAO). We evaluate the performance of these datasets in reproducing the temporal clustering in the observations using Cox regression. The results of this work indicate that: (1) heavy precipitation events over Europe exhibit temporal clustering; (2) all of the reanalysis products we have considered are capable of well reproducing the temporal clustering exhibited by the observations, and in general JRA-55 performs the best; moreover, our findings indicate that MERRA2 performs better than its predecessor MERRA in representing the relationship between heavy precipitation and these two climate indices; (3) all the reanalysis products in general tend to agree in terms of the temporal variability of NAO/AO.

Inter-comparing and improving land surface temperature estimates from passive microwaves over the Jiangsu province of the People’s Republic of China

ABSTRACT In the recent years, the FengYun-3B passive microwave observations (FY3B) have been used to study global and regional soil moisture estimates, and land surface temperature (LST) has been an important input in the retrieval model. Previous studies proposed a linear model to derive LST from Ka-band brightness temperatures (H09 approach) which were routinely masked based on open water data. This study aimed to further understand microwave emission under such challenging open water conditions and developed the first data-driven approach to correct for such open water conditions. The H09 was first applied to the FY3B observations, and then inter-compared against MODIS (Moderate-resolution Imaging Spectro-radiometer), MERRA (Modern Era Retrospective analysis for Research and Applications) and ground observations over the Jiangsu province in the People’s Republic of China. Several freshwater bodies are located within this region, and the Pacific Ocean bounds at the east, this makes it an excellent region for the study of open water impact on the passive microwave observations. As in previous studies, the quality of the FY3B LST based on the H09 approach dropped over the regions with high open water. While the MERRA and MODIS LST products maintained an average correlation of about 0.95, the FY3B LST product had an average correlation of 0.85 with drops to about 0.65 over the open water regions. Additional comparisons also revealed biases over the open water regions within the passive microwave product. An open water metric, which is based on the percentage of a pixel and its surrounding pixels covered by water was developed to quantify the impact of open water. Based on a pixel-wise linear relationship between the MERRA LST and FY3B Ka-band brightness temperature, spatially variable slopes and intercepts were obtained as a function of open water to produce a correction for open water in the H09 which leads to the HG18 approach. Validations with both the MODIS LST product and the ground observations showed that there was a significant reduction in both the absolute bias (MODIS: 2 K, in situ: 10 K) and the standard errors (MODIS: 0.8 K, in situ: 1 K) using the HG18 approach. This opens up a new window towards further improving and understanding LST retrievals from passive microwave products.

Past, Present and Future Climate Trends Under Varied Representative Concentration Pathways for a Sub-Humid Region in Uganda

Long-term trend analysis at local scale for rainfall and temperature is critical for detecting climate change patterns. This study analysed historical (1980–2009), near future (2010–2039), mid- (1940–2069) and end-century (2070–2099) rainfall and temperature over Karamoja sub-region. The Modern Era-Retrospective Analysis for Research and Applications (MERRA) daily climate data provided by the Agricultural Model Inter-comparison and Improvement Project (AgMIP) was used. The AgMIP delta method analysis protocol was used for an ensemble of 20 models under two representative concentration pathways (RCPs 4.5 and 8.5). Historical mean rainfall was 920.1 ± 118.9 mm and minimum, maximum and mean temperature were 16.8 ± 0.5 °C, 30.6 ± 0.4 °C and 32.0 ± 0.7 °C, respectively. Minimum temperature over the historical period significantly rose between 2000 and 2008. Near future rainfall varied by scenario with 1012.9 ± 146.3 mm and 997.5 ± 144.7 mm for RCP4.5 and RCP8.5 respectively; with a sharp rise predicted in 2017. In the mid-century, mean annual rainfall will be 1084.7 ± 137.4 mm and 1205.5 ± 164.9 mm under RCP4.5 and RCP8.5 respectively. The districts of Kaabong and Kotido are projected to experience low rainfall total under RCP4.5 (mid-century) and RCP8.5 (end-century). The minimum temperature is projected to increase by 1.8 °C (RCP4.5) and 2.1 °C (RCP8.5) in mid-century, and by 2.2 °C (RCP4.5) and 4.0 °C (RCP8.5) in end-century.